Multi-Agent AI & RAG Knowledge System for Real Estate

The challenge: Design a complete agentic AI architecture for a real-estate marketplace — one capable of serving buyers, sellers/brokers, and document-verification workflows through specialized AI agents, grounded in real data rather than free-floating LLM responses.

What we built: A full multi-agent system design comprising a fleet of specialized agents (a buyer-facing conversational concierge, a seller/broker AI co-pilot, a document/listing verification agent, and a pricing-valuation agent), coordinated through a central model-routing and orchestration layer. The system is grounded by a Retrieval-Augmented Generation (RAG) pipeline spanning multiple structured knowledge bases (legal/regulatory corpus, market pricing data, locality/infrastructure data) plus a graph-based knowledge layer linking listings, agents, leads, and locations for relationship-aware retrieval — with human-in-the-loop approval gates on consequential actions.

The outcome: A complete, implementation-ready technical architecture covering agent orchestration, RAG pipeline design (vector + graph retrieval), model-routing strategy, and safety guardrails — the kind of specification an engineering team can build directly from.